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Using the contribution matrix to evaluate complex study limitations in a network meta-analysis: a case study of bipolar maintenance pharmacotherapy review

Overview of attention for article published in BMC Research Notes, April 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

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13 X users

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Title
Using the contribution matrix to evaluate complex study limitations in a network meta-analysis: a case study of bipolar maintenance pharmacotherapy review
Published in
BMC Research Notes, April 2016
DOI 10.1186/s13104-016-2019-1
Pubmed ID
Authors

Toshi A. Furukawa, Tomofumi Miura, Anna Chaimani, Stefan Leucht, Andrea Cipriani, Hisashi Noma, Hiroshi Mitsuyasu, Shegenobu Kanba, Georgia Salanti

Abstract

Limitations in the primary studies constitute one important factor to be considered in the grading of recommendations assessment, development, and evaluation (GRADE) system of rating quality of evidence. However, in the network meta-analysis (NMA), such evaluation poses a special challenge because each network estimate receives different amounts of contributions from various studies via direct as well as indirect routes and because some biases have directions whose repercussion in the network can be complicated. In this report we use the NMA of maintenance pharmacotherapy of bipolar disorder (17 interventions, 33 studies) and demonstrate how to quantitatively evaluate the impact of study limitations using netweight, a STATA command for NMA. For each network estimate, the percentage of contributions from direct comparisons at high, moderate or low risk of bias were quantified, respectively. This method has proven flexible enough to accommodate complex biases with direction, such as the one due to the enrichment design seen in some trials of bipolar maintenance pharmacotherapy. Using netweight, therefore, we can evaluate in a transparent and quantitative manner how study limitations of individual studies in the NMA impact on the quality of evidence of each network estimate, even when such limitations have clear directions.

X Demographics

X Demographics

The data shown below were collected from the profiles of 13 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 17%
Researcher 4 13%
Student > Bachelor 3 10%
Other 3 10%
Professor 2 7%
Other 5 17%
Unknown 8 27%
Readers by discipline Count As %
Medicine and Dentistry 7 23%
Pharmacology, Toxicology and Pharmaceutical Science 2 7%
Mathematics 2 7%
Nursing and Health Professions 1 3%
Philosophy 1 3%
Other 4 13%
Unknown 13 43%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 May 2016.
All research outputs
#3,978,524
of 22,862,742 outputs
Outputs from BMC Research Notes
#586
of 4,267 outputs
Outputs of similar age
#62,689
of 300,620 outputs
Outputs of similar age from BMC Research Notes
#16
of 100 outputs
Altmetric has tracked 22,862,742 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,267 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 86% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 300,620 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 100 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.